The Evolution of Wildfires: California

Wildfires Throughout the Years (1992-2020)

In the last several decades there has been a noticeable trend regarding the increase in fires in the United States. Not just in the States, but in the world as a whole. People are concerned, and for good reason– when the land is dry they can spread quickly and be hard to contain.

California especially has been a hot topic regarding its fire situation. California has been known for years to have many fires, but what has been changing to make this situation more dangerous? What trends can be found from looking at this data? And are fires really becoming more dangerous or are we only just now paying more attention?

Accessing the Data

The data I am using for this project is from the US Department of Agriculture (more specifically, Karen C. Short) and can be found here: https://www.fs.usda.gov/rds/archive/catalog/RDS-2013-0009.6

Preparing the Data

The dataset I am working with is quite large at 2,300,000+ occurrences. As such, I found the need to break the data up into smaller subsets so that it is easier to work with. During this process I created a few new variables: discovery, containment, fire_duration, and fire_severity.

discovery - combines the date and time of day for when a fire was discovered.
containment - combines the date and time of day for when a fire was contained.
fire_duration - calculates how long a fire lasts from discovery to containment in minutes.
fire_severity - calculated by multiplying the size of the fire by its duration.

source("wildfires_wrangling.R")

Finally, Working With the Data

# creating a basic bar plot to view the number of fires (by size) over the years
ggplot(fod_CA, aes(x = fire_year, fill = fire_size_class)) +
  geom_bar()  +
  labs(
    title = "30 Years of Californian Wildfires",
    x = "Year",
    y = "Count",
    fill = "Fire Size Class",
    caption = "Data provided by USDA") +
  scale_fill_brewer(palette="YlOrRd")

Given the above bar chart it could be easy to dismiss the big question. While 2020 is the most recent year it hasn’t been the year with the most fires. No, that honor falls to 2007. However, if we look closer at fires of size G (which are 5000+ acres in size) there may be a more interesting trend.

# filtering the data 
fod_CA_G <- fod_CA |> filter(fire_size_class == "G")
# making essentially the same plot, but focusing on the largest fire sizes
ggplot(fod_CA_G, aes(x = fire_year, fill = fire_size_class)) +
geom_bar()  +
  labs(
    title = "California's Largest Fires",
    x = "Year",
    y = "Count",
    fill = "Fire Size Class",
    caption = "Data provided by USDA")

Now let’s bring this back and look at not just the size classification, but the actual size of the fires throughout the years.

ggplot(fod_CA, aes(x = fire_year, y = fire_size, color = fire_size_class)) +
  geom_point()  +
  labs(
    title = "California's Largest Fires: Take 2",
    x = "Year",
    y = "Size",
    color = "Fire Size Class",
    caption = "Data provided by USDA")+
  scale_color_brewer(palette="YlOrRd")

So How do Severity and Duration factor in?

Using the fire_duration and fire_severity variables I created earlier I calculated the averages across a handful of years.

year <- c(1992,1995,2000,2005,2010,2015,2016,2017,2018,2019,2020)

size <- c(
  mean(CA_1992$fire_size, na.rm=TRUE),
  mean(CA_1995$fire_size, na.rm=TRUE),
  mean(CA_2000$fire_size, na.rm=TRUE),
  mean(CA_2005$fire_size, na.rm=TRUE),
  mean(CA_2010$fire_size, na.rm=TRUE),
  mean(CA_2015$fire_size, na.rm=TRUE),
  mean(CA_2016$fire_size, na.rm=TRUE), 
  mean(CA_2017$fire_size, na.rm=TRUE), 
  mean(CA_2018$fire_size, na.rm=TRUE),
  mean(CA_2019$fire_size, na.rm=TRUE),
  mean(CA_2020$fire_size, na.rm=TRUE))

duration <- c(
  mean(CA_1992$fire_duration, na.rm=TRUE),
  mean(CA_1995$fire_duration, na.rm=TRUE),
  mean(CA_2000$fire_duration, na.rm=TRUE),
  mean(CA_2005$fire_duration, na.rm=TRUE),
  mean(CA_2010$fire_duration, na.rm=TRUE),
  mean(CA_2015$fire_duration, na.rm=TRUE),
  mean(CA_2016$fire_duration, na.rm=TRUE), 
  mean(CA_2017$fire_duration, na.rm=TRUE), 
  mean(CA_2018$fire_duration, na.rm=TRUE),
  mean(CA_2019$fire_duration, na.rm=TRUE),
  mean(CA_2020$fire_duration, na.rm=TRUE))

severity <- c(
  mean(CA_1992$fire_severity, na.rm=TRUE),
  mean(CA_1995$fire_severity, na.rm=TRUE),
  mean(CA_2000$fire_severity, na.rm=TRUE),
  mean(CA_2005$fire_severity, na.rm=TRUE),
  mean(CA_2010$fire_severity, na.rm=TRUE),
  mean(CA_2015$fire_severity, na.rm=TRUE),
  mean(CA_2016$fire_severity, na.rm=TRUE), 
  mean(CA_2017$fire_severity, na.rm=TRUE), 
  mean(CA_2018$fire_severity, na.rm=TRUE),
  mean(CA_2019$fire_severity, na.rm=TRUE),
  mean(CA_2020$fire_severity, na.rm=TRUE))

df <- data.frame(year, size, duration, severity)
df |> arrange(desc(severity))

   year      size  duration   severity
1  2020 417.08324 2581.1955 36539834.5
2  2018 172.40278 1009.9193 19888390.8
3  2015 115.35523 1828.7477  7998538.3
4  2016  72.93364  708.2207  6815913.4
5  2019  45.64936 2585.4358  4540254.9
6  2017 142.61927 1267.2733  4231720.2
7  2000  37.97286  526.9906  1427894.1
8  2010  15.40284 1861.9513  1140790.2
9  1992  27.36955  693.9354   606623.1
10 1995  29.27364  653.2748   582316.9
11 2005  25.83049  591.6193   451918.4

Here we can see that 2020, the most recent year, is coming out on top with the most severe fires on average. Not only that, but all of the more recent years are showing to have more severe fires.

# displaying the above df as a connected scatter plot
ggplot(df, aes(x = year, y = severity)) +
  geom_line(color = "grey") +
  # adding red circles at the datapoints 
  geom_point(shape = 21, color = "black", fill = "red", size = 6) +
  labs(
    title = "Evolution of Fire Severity",
    x = "Year",
    y = "Severity",
    caption = "Data provided by USDA")

Gaining Perspective

# selecting which feature IDs to show on the map 
IDs <- c("oid","latitude","longitude","state","fips_name",
         "owner_descr","fire_code", "fire_name","discovery",
         "containment","fire_duration","fire_severity","fire_size",
         "fire_size_class","nwcg_cause_classification",
         "nwcg_general_cause","nwcg_cause_age_category")

# creating an interactive map that will plot CA fires from 2020 via latitude and longitude
# each point on the map is clickable and will pull up features associated with that point 
mapviewOptions(basemaps = "OpenStreetMap.DE") #<- limits which maps can be chosen
map_2020 <- CA_2020 |> mapview(
  xcol = "longitude",
  ycol = "latitude",
  zcol = "fire_size_class", # coloring by fire_class
  col.regions =  brewer.pal(7, "YlOrRd"),
  cex = "fire_size", # points on the map will vary by fire size (in acres)
  crs = "NAD83", # coordinate system
  grid = FALSE,
  popup = popupTable(CA_2020, zcol = IDs),
  layer.name = "Fire Size Class",
  legend = TRUE)
map_2020

# same deal, different years
map_1992 <- CA_1992|> mapview(
  xcol = "longitude",
  ycol = "latitude",
  zcol = "fire_size_class",
  col.regions =  brewer.pal(7, "YlOrRd"),
  cex = "fire_size",
  crs = "NAD83",
  grid = FALSE,
  popup = popupTable(CA_2020, zcol = IDs),
  layer.name = "Fire Size Class")
map_1992

These maps only cover two different years, but they do a great job of highlighting what all the data has been saying so far:

Fires in California are getting worse, and to a fairly significant degree. The amount of fires may not be at it’s highest, but the intensity and size are growing at a concerning rate.

In this project I was only able to explore this data to a small degree. Given more time I would like to figure out why the fires are getting worse and if there are similar trends being shown across the other states. Since we’re given coordinates as well as time of year for each fire it would interesting to see what environmental factors come into play as well. How many fires are started by humans vs nature? How much of a role does nature still play in the human-caused fires?